auditledge

v0.1.1 suspicious
4.0
Medium Risk

Official Python SDK for Auditledge — Audit Log API for SaaS developers

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate network activity and is newly released with no maintainer history, raising concerns about its legitimacy and potential for misuse.

  • moderate network risk
  • lack of maintainer history
Per-check LLM notes
  • Network: The package makes network requests which could indicate legitimate functionality like fetching updates or external data, but without further context, it's hard to rule out potential misuse.
  • Shell: No shell execution patterns detected, suggesting a lower risk of direct command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The package is new and lacks a maintainer history, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4454 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 6 type-annotated function signatures (partial)
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • dy else None req = urllib.request.Request( url, data=data,
  • try: with urllib.request.urlopen(req, timeout=self.timeout) as response:
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: auditledge.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Auditledge" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with auditledge 
Develop a comprehensive mini-application named 'AuditLogAnalyzer' using Python that leverages the 'auditledge' package to provide real-time monitoring and analysis of audit logs from various SaaS applications. This tool will help system administrators and security analysts to efficiently track user activities and detect potential security threats or compliance issues.

Step 1: Setup Environment
- Install necessary packages including 'auditledge', 'pandas', and 'matplotlib'.

Step 2: Data Collection
- Utilize the 'auditledge' package to fetch audit logs from multiple SaaS platforms.
- Implement a function that schedules periodic log fetching (e.g., every hour).

Step 3: Data Processing
- Clean and preprocess the collected data using pandas.
- Define functions to parse different types of log entries and categorize them (e.g., login/logout, file access, etc.).

Step 4: Real-Time Monitoring
- Develop a real-time dashboard that visualizes key metrics such as total number of logins, failed login attempts, etc., using matplotlib.
- Integrate alerting mechanisms to notify users via email/SMS if certain thresholds are exceeded (e.g., more than 5 failed login attempts in an hour).

Step 5: Advanced Analysis
- Implement machine learning models to predict potential security threats based on historical log data.
- Provide a feature to generate detailed reports on user activities over specific time periods.

Suggested Features:
- User-friendly GUI built with Tkinter or Streamlit.
- Integration with popular cloud services like AWS S3 for storing log data.
- Option to export analysis results to CSV or PDF formats.
- Support for multi-language logs and internationalization.

How 'auditledge' is Utilized:
- The 'auditledge' package is primarily used for fetching audit logs from SaaS applications through its API. It simplifies the process of integrating with different SaaS platforms by providing a unified interface for log retrieval. Additionally, it offers functionalities for filtering and processing raw log data, which are essential for building the real-time monitoring and advanced analysis components of the application.

💬 Discussion Feed

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